Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

Noor Aboueleneen; Abdulmalik Alwarafy; Mohamed Abdallah

doi:10.1109/IWCMC58020.2023.10182964

Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

Noor Aboueleneen
, Abdulmalik Alwarafy
, Mohamed Abdallah

Department of Computer and Network Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones' transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.

Original language	English
Title of host publication	2023 International Wireless Communications and Mobile Computing, IWCMC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	818-823
Number of pages	6
ISBN (Electronic)	9798350333398
DOIs	https://doi.org/10.1109/IWCMC58020.2023.10182964
Publication status	Published - 2023
Event	19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023 - Hybrid, Marrakesh, Morocco Duration: Jun 19 2023 → Jun 23 2023

Publication series

Name	2023 International Wireless Communications and Mobile Computing, IWCMC 2023

Conference

Conference	19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023
Country/Territory	Morocco
City	Hybrid, Marrakesh
Period	6/19/23 → 6/23/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

deep reinforcement learning
energy efficiency
Internet of drones (IoD)
security

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Safety, Risk, Reliability and Quality

Access to Document

10.1109/IWCMC58020.2023.10182964

Cite this

Aboueleneen, N., Alwarafy, A., & Abdallah, M. (2023). Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach. In 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (pp. 818-823). (2023 International Wireless Communications and Mobile Computing, IWCMC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWCMC58020.2023.10182964

Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach. / Aboueleneen, Noor; Alwarafy, Abdulmalik; Abdallah, Mohamed.
2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 818-823 (2023 International Wireless Communications and Mobile Computing, IWCMC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Aboueleneen, N, Alwarafy, A & Abdallah, M 2023, Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach. in 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. 2023 International Wireless Communications and Mobile Computing, IWCMC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 818-823, 19th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2023, Hybrid, Marrakesh, Morocco, 6/19/23. https://doi.org/10.1109/IWCMC58020.2023.10182964

Aboueleneen N, Alwarafy A, Abdallah M. Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach. In 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 818-823. (2023 International Wireless Communications and Mobile Computing, IWCMC 2023). doi: 10.1109/IWCMC58020.2023.10182964

Aboueleneen, Noor ; Alwarafy, Abdulmalik ; Abdallah, Mohamed. / Secure and Energy-Efficient Communication for Internet of Drones Networks : A Deep Reinforcement Learning Approach. 2023 International Wireless Communications and Mobile Computing, IWCMC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 818-823 (2023 International Wireless Communications and Mobile Computing, IWCMC 2023).

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abstract = "Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones' transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.",

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AB - Internet of Drones (IoD)-aided wireless networks are proving their efficiency in various commercial and military applications, such as object recognition, surveillance, and data acquisition. However, the broadcast communication nature of IoD networks raises significant communication security issues. This paper investigates drone-to-ground communication subject to eavesdroppers in urban environments. We aim to provide secure communication utilizing physical layer security by increasing network secrecy rates. In addition, we aim to reduce the energy consumption within the IoD network by optimizing drones' transmitting and jamming power and employing energy harvesting techniques to charge drones wirelessly. Our optimization problem is formulated as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm is proposed to solve the problem.

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Secure and Energy-Efficient Communication for Internet of Drones Networks: A Deep Reinforcement Learning Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this